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Biology
Computer Science

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meTCRs - Learning a metric for T-cell receptors

T cell receptors (TCRs) bind to pathogen- or self-derived epitopes to elicit a T cell response as part of the adaptive immune system. Determining the specificity of TCRs provides context for immunological studies and can be used to identify candidates for novel immunotherapies. To avoid costly experiments, large-scale TCR-epitope databases are queried for similar sequences via various distance functions. Here, we developed the deep-learning based distance meTCRs. Contrary to most previous approaches, the method avoids computational expansive pairwise string operations by comparing TCRs in a numeric embedding. In contrast to models which are trained specificity-agnostic, we directly utilize epitope information by applying deep metric learning to guide the training. Summarizing, we present meTCRs as a scalable alternative to embed TCR repertoires for clustering, visualisation, and querying against the ever-increasing amount TCR-epitope pairs in publicly available databases.

Introduction
T cells recognize epitopes, i.e., peptide sequences derived from antigens, via the highly variable T cell receptor (TCR). Due to the immense diversity, the adaptive immune system is able to fight a wide variety of diseases including infections and tumors: At least 10 7 different TCR sequences exist in an individual human being, while population wide estimation exceed 10 20 TCRs [1]. Through recent advances in single-cell technology, T cell specificity can now be determined in large TCR repertoires for predefined epitopes [2]. Early studies have shown, that TCRs with similar sequences are likely bind to the same epitope [3,4]. Following, various approaches were developed to cluster TCR sequences based on their specificity, or to perform database queries by calculating pairwise sequence similarity. Most approaches are either based on string-based comparison [5,6,7] or numeric embeddings [8,9]. Typically, string-base approaches make use of heuristic similarity measurements, which often rely of computationally costly pairwise string operations. While this is partially avoided for numeric representations, they usually rely on unsupervised approaches such as biophysical-informed embeddings or Variational Autoencoders, which are not specifically trained to preserve epitope specificity. To circumvent these shortcomings, we propose meTCRs -a numeric TCR embedding, which incorporates specificity annotation during training by applying supervised deep metric learning [10]. We envision meTCRs to become a valuable tool to derive meaningful representations of TCRs, which seamlessly scale to downstream tasks such as clustering on large-scale single-cell datasets or querying vast databases to assign specificity. Furthermore, the method can continuously improve by incorporating novel developments in the field of deep metric learning and by the ever-increasing amount of publicly available training data.

Method
Data pre-processing: Paired epitope and TCR sequencing data were obtained from three publicly available databases IEDB [11], VDJdb [12], and McPAS-TCR [13] for training and validation. More specifically, we extracted the hypervariable complementarity-determining region 3 of the beta-chain (CDR3-β) of the TCR as it lays in close proximity to the epitope and is assumed to be the driving factor in antigen recognition [14] and is therefore most prevalent in those databases. To harmonize the datasets, leading cysteine and ending phenylalanine or tryptophan were removed from the CDR3-β sequences. The amino acid sequences were end-padded to the maximal sequence length (l = 36) in the dataset and one-hot encoded. The epitope annotations were solely used as categorical labels.
be a dataset consisting of the pre-processed CDR3-β sequence x k T CR ∈ A l and the categorical binding label b k ∈ B for each database entry k, where A represents the set of possible amino acids and padding character, and B corresponds to the set of all binding epitopes. meTCRs consists of an embedding model E, which takes the x k T CR as an input. As embedding model, we implemented multilayer perceptrons (MLPs), convolutional neural networks (CNNs), long short-term memory networks (LSTMs) [15], and transformers [16]. For the first three architectures the one-hot encoded CDR3-β sequence is directly used as input. For the transformer encoder, an additional position-wise encoding was added to a learnt embedding layer. Eventually, a final linear layer is applied to the output of the last network layer. Thereby, the embedding network projects the input data into the representation h i T CR = E(x i T CR ) ∈ R d emb with the embedding dimension d emb on which all downstream tasks are performed.
Loss function: Most deep metric learning losses are calculated on batches of both positive and negative pairs [10]. Typically, selecting informative tuples during training is a challenging design choice. In the case of meTCRs, a pair of TCRs is a positive pair if the receptors of database entry A and B bind on a common epitope (b A = b B ) and negative if this is not the case (b A ̸ = b B ). However, common TCR databases only contain experimentally validated epitope bindings and miss negative pairs. Furthermore, TCRs might be cross-reactive, i.e., a TCR may recognize multiple epitopes. Therefore, randomly creating samples by pairing TCRs from different epitope classes can create false-negative pairs. The Barlow Twin loss circumvents these challenges [17]. Here, the cross correlation matrix of batch-normalized inputs is calculated on positive pairs only. Overall, the Barlow Twin loss maximizes the similarity between paired representation while the embedding's redundancy is minimized. Batch normalization ensured that no trivial representation is learned even though only positive pairs are used.
Training Details: The models were trained on the processed data from IEDB and VDJdb excluding duplicates, which resulted in N=145,331 entries. Pairs of (X k T CR , b k ) were randomly divided into training and validation set with a split of 80%-20%. Due to the nature of the Barlow Twin loss, the batches need to be composed of paired input sequences. To this end, epitope classes which contained only one TCR sequence within a set were removed. During training, hierarchical sampling is applied to build batches of size N . In the first step, N 2 epitopes are sampled randomly and proportional to the epitope distribution within the training set to prevent over-representation of small classes. Following,  [18]. All hyperparameter of the embedding model, the optimizer, and the loss function were optimized via the Tree-Structured Parzen Estimator (TPE) algorithm [19] as implemented in the Optuna framework [20] to maximize the mean average precision (MAP) [10] at 10 on the validation set (see Results).

Results
We generated an independent set from the processed McPAS-TCR database by removing all pairs which already appear in the training or validation set from the IEDB and VDJdb and epitopes with only one reported TCR, which resulted in a test set T of N=7,733. Note, that prediction on this set are especially challenging due to a shift in epitopes between databases. Only two out of the five most abundant epitopes in the training set are present in the test set, and two out of the five largest classes in the test set are available in the training set with more than 50 TCRs. We embedded the test set via the different versions of the embedding network E, followed by calculating pairwise distances using the Euclidean norm. Additionally, pairwise distances are derived from TCRmatch as a string-based baseline method.
To measure the ability to predict specificity with meTCRs, we report classification based metrics.
Here, the k-nearest neighbors F R q are determined for each element q of the test set based on the pairwise distances. Following, the metrics for F1-score (F1@R), precision (P@R), and recall (R@R) at R = 10 are reported. While R@R describes the average of samples for which a correct pair is contained within the R nearest neighbors, P@R is calculated as the average amount of matching samples within R nearest neighbors. F1@R is calculated as the harmonic mean between R@R and P@R. Further, we report the mean average precision at R ∈ {1, 10} as proposed in [10] which incorporates the rank of the retrieval. To evaluate the consistency of meTCRs' embedding space, we cluster the embedded samples via the K-Means algorithm with K equal to the number of epitope classes in the test set and report the normalized mutual information (NMI). Finally, we report the AUC-ROC as a threshold based evaluation metric.
The choice of embedding network heavily influenced the models' performance. The transformerbased model surpassed the remaining models by a large margin on all classification and clustering metrics (Table 1). However, meTCRs still falls short compared to the string-comparison based method TCRmatch by approximately one point across all metrics. In this evaluation, the predicted specificity by meTCRs and TCRmatch corresponds to the true specificity in approximately one third of the database queries. While this performance is not yet sufficient to accurately determine the specificity of TCRs, it provides researchers with a valuable initial guess for further experimental validation.
Similar trends between the different embedding networks were also observed to a lesser degree for AUC-ROC based evaluation across the five most abundant epitopes (Table 2), except for the LSTMbased model, which surprisingly outperformed the transformer-model on the two most abundant epitopes and on the weighted average. Interestingly, meTCRs outperformed TCRmatch by large margins for most classes with at least one model version. Further, there is a large discrepancy in the performance for different epitopes. It needs to be further evaluated, whether the low similarity for these epitopes are inherent to the dataset and why different versions of the models fail to generalize to for individual classes.

Discussion
Here, we introduced meTCRs for clustering and querying databases of TCRs as an alternative to classical string metrics or novel unsupervised approaches. Contrary to previous methods, meTCRs directly utilizes epitope binding information during training of a TCR distance by applying deep metric learning. The method can be easily scaled to large datasets or databases by pre-computing the numeric representation for each TCR, followed by the calculation of the computational cheap Euclidean pairwise distances on these embeddings.
In future work, the method can further be improved by adjusting for the imbalance in the databases by different sample schemes, loss weigthing, or further model adaptation. Exemplary, the IEDB, as largest source of TCR-epitope pairs, contains a total of 939 different classes. However, 49% of the TCRs stem from one of eight epitopes. Second, the performance can further be improved by collecting the ever-increasing amount of publicly available binding data. In previous studies, additional information on the TCR such as the α-chain, or V(D)J-gene annotation benefited TCRepitope prediction and clustering [3]. However, full TCR-sequence data is sparse in public databases. Therefore, the model architecture could be adjusted for the missing value problem to embed TCRs with various degrees of information. Lastly, the presented evaluation highlights, that meTCRs generalizes well to unseen epitopes. However, an in-depth validation of meTCRs is yet to be performed for a wider variety of diseases and use cases. One major application of the method will be queries to databases, where most of the epitopes were already observed by the model during training. Therefore, meTCRs needs to be thoroughly compared to common methods like TCRmatch [6], TCRdist [5], and DeepTCR [8] for performance and computational cost in this use-case.
Upon these improvements, we envision meTCRs to become a fast and scalable solution for providing a specificity-informed representation of TCR repertoires that enables downstream tasks such as visualisation and clustering. Especially the ability to conduct computationally efficient queries to large TCR-epitope databases, provides a compelling alternative to determine specificity of TCR sequences from low-throughput experiments and large-scale single-cell studies without the need for exhaustive wet-lab experiments.

Data and code availability
The corresponding data are publicly available and were downloaded from IEDB (https://www.


Identification of cis-regulatory modules for adeno-associated virus-based cell-type-specific targeting in the retina and brain

Adeno-associated viruses (AAVs) targeting specific cell types are powerful tools for studying distinct cell types in the central nervous system (CNS). Cis-regulatory modules (CRMs), e.g., enhancers, are highly cell-type-specific and can be integrated into AAVs to render cell type specificity. Chromatin accessibility has been commonly used to nominate CRMs, which have then been incorporated into AAVs and tested for cell type specificity in the CNS. However, chromatin accessibility data alone cannot accurately annotate active CRMs, as many chromatin-accessible CRMs are not active and fail to drive gene expression in vivo. Using available large-scale datasets on chromatin accessibility, such as those published by the ENCODE project, here we explored strategies to increase efficiency in identifying active CRMs for AAV-based cell-type-specific labeling and manipulation. We found that prescreening of chromatin-accessible putative CRMs based on the density of cell-type-specific transcription factor binding sites (TFBSs) can significantly increase efficiency in identifying active CRMs. In addition, generation of synthetic CRMs by stitching chromatin-accessible regions flanking cell-type-specific genes can render cell type specificity in many cases. Using these straightforward strategies, we generated AAVs that can target the extensively studied interneuron and glial cell types in the retina and brain. Both strategies utilize available genomic datasets and can be employed to generate AAVs targeting specific cell types in CNS without conducting comprehensive screening and sequencing experiments, making a step forward in cell-type-specific research.

Adeno-associated viruses (AAVs) targeting specific cell types are powerful tools for studying distinct cell types in the central nervous system (CNS). Cis-regulatory modules (CRMs), e.g., enhancers, are highly cell-type-specific and can be integrated into AAVs to render cell type specificity. Chromatin accessibility has been commonly used to nominate CRMs, which have then been incorporated into AAVs and tested for cell type specificity in the CNS. However, chromatin accessibility data alone cannot accurately annotate active CRMs, as many chromatin-accessible CRMs are not active and fail to drive gene expression in vivo. Using available large-scale datasets on chromatin accessibility, such as those published by the ENCODE project, here we explored strategies to increase efficiency in identifying active CRMs for AAV-based cell-typespecific labeling and manipulation. We found that prescreening of chromatin-accessible putative CRMs based on the density of cell-type-specific transcription factor binding sites (TFBSs) can significantly increase efficiency in identifying active CRMs. In addition, generation of synthetic CRMs by stitching chromatin-accessible regions flanking cell-typespecific genes can render cell type specificity in many cases. Using these straightforward strategies, we generated AAVs that can target the extensively studied interneuron and glial cell types in the retina and brain. Both strategies utilize available genomic datasets and can be employed to generate AAVs targeting specific cell types in CNS without conducting comprehensive screening and sequencing experiments, making a step forward in cell-type-specific research.
Molecular tools that allow for cell-type-specific labeling and manipulation in the central nervous system (CNS) are important for understanding how this complex tissue works and responds to disease. Adeno-associated viruses (AAVs), which can provide safe and long-lasting expression, offer a powerful way to target CNS cells in a rapid, cost-effective, and efficient manner (1, 2). Targeted AAV injections into different regions of the CNS have been widely used to label and study CNS cells and diseases (3,4). The challenge is to develop recombinant AAV-based tools that allow for cell-type-or subtype-specific labeling and manipulation. One of the major strategies for developing such AAVs is to integrate cis-regulatory modules (CRMs) that can control cell-type-and stagespecific gene expression into AAV vectors. This strategy restricts the expression of genes carried by AAVs to specific cell types without changing the tropism of AAVs. Multiple methods have been developed to screen for cell-type-specific CRMs based on chromatin accessibility, genomic DNA methylation pattern, sequence conservation, and transcription factor binding sites (TFBSs) (5)(6)(7)(8)(9)(10). However, most of these methods rely on performing genome-wide sequencing, such as single-cell ATAC-seq (scATAC-seq), or large-scale screening. The efficiency of identifying cell-type-specific CRMs for AAVs has also not been high, especially for nonabundant cell types in the CNS. As many large-scale datasets on chromatin accessibility, e.g., ENCODE project (11,12), are available for CNS tissues, efficient strategies that utilize these datasets should significantly benefit the generation of cell-type-specific AAV tools. The neural retina serves as an excellent model system for developing such strategies. It is composed of more than a hundred distinct cell types/subtypes and can be easily accessed via surgical techniques (13)(14)(15)(16)(17). Using this model, we explored strategies that can efficiently identify CRMs for AAV-based cell-type-specific targeting and applied them to the brain.
We nominated putative CRMs based on chromatin accessibility as revealed by the ENCODE project and published ATAC-seq datasets for the retina and brain (12,18). We performed a pilot study to search for common features of active CRMs and found that bioinformatically prescreening of chromatin accessible putative CRMs based on the density of cell-type-specific TFBSs can significantly increase the efficiency of identifying active CRMs. For cell types with limited information, generation of synthetic CRMs by stitching chromatin accessible regions flanking cell-type-specific genes can often straightforwardly render cell type specificity without conducting complicated screens. Using these strategies, we generated AAVs that target extensively studied CNS cell types, including Müller glial cells and amacrine interneurons in the retina, and astrocytes and parvalbumin (PV)-positive interneurons in adult brain.

Results
Cell-type-enriched TFBS density can be used to predict the activity of CRMs Chromatin accessibility data alone cannot accurately annotate active CRMs. Many of the chromatin "open" CRMs are not active and fail to drive gene expression in vivo (19). To improve the efficiency of identifying active CRMs, we conducted a pilot study in which we tested the activities of chromatin accessible putative CRMs in the retina and compared the common features of active CRMs with those of inactive CRMs. We focused on retinal Müller glial cells (MG) because MG-enriched genes and a chromatin accessibility map of MG can be readily obtained from published single-cell RNA-seq and ATAC-seq datasets (17,18). In addition, MGs play essential roles in maintaining the homeostatic environment of the retina (20). Recent studies further showed that MG can be reprogrammed into neurons in vivo, offering a promising treatment for retinal degenerative diseases (18,21,22). Therefore, identification of MG-specific CRMs and AAVs could be useful for studying MG under normal and disease conditions. We collected 19 chromatin accessible regions flanking five wellknown MG-enriched genes, including Rlbp1, Clu, Glul, Aqp4, and Ckdn1b (23), based on published ATAC-seq from purified mouse MG (Fig. 1A). These putative CRMs were individually cloned into Stagia3 reporter plasmid, which expresses alkaline phosphatase (AP) when active CRM is inserted in front of the minimal TATA promoter (24,25). The plasmids harboring different putative CRMs were electroporated into mouse retinas ex vivo at postnatal day 1 (P1). Electroporation mainly delivers plasmid DNA into mitotic retinal progenitor cells as nuclear envelop breakdown is necessary for the plasmids to enter the nuclei (26). P1 was chosen because retinal progenitor cells can differentiate into MG at this stage. The activities of CRMs were determined by AP staining 5 days later when retinal cell fate specification is largely finished. Four out of the 19 putative CRMs showed strong activity in the retina (Fig. 1B). We compared the sequences of active CRMs with inactive CRMs and tried to extract features correlating with CRM activity. Sequence conservation, ATAC-seq/DNase-seq peak height, and the number and density of total TFBSs within the CRM did not show any correlations with the activity of CRMs (Fig. S1, A-E). Interestingly, when we collected the TFBS matrices of MG-enriched TFs and scanned the 19 CRMs with these MG-TFBSs bioinformatically, the number of MG-TFBSs per 100 bp was positively correlated with the activity of CRMs (Fig. 1, C and D). This result suggests that prescreening of chromatin accessible putative CRMs based on the density of MG-TFBSs may significantly increase the efficiency of identifying active CRMs.
We further tested this prescreening strategy in retinal MG by increasing the scale of the screen (Fig. 1, E-G). We collected additional MG-enriched genes based on published single-cell RNA-seq data (16,17) and literature (23), and selected putative CRMs flanking these MG-enriched genes based on chromatin accessibility revealed by ATAC-seq on MG (18) (Fig. 1E). The chromatin accessible regions flanking the genes specifically expressed in bipolar (BP), amacrine (AC), or photoreceptor (PR) cells were selected from the MG ATAC-seq data to serve as controls (Fig. 1E). We scanned a total of 149 putative CRMs with MG-TFBS matrices bioinformatically. The number of MG-TFBSs detected per 100 bp was presented in a two-way plot for each CRM (Fig. 1F). We randomly selected putative CRMs in quadrant A1-A3 or fell on the threshold lines of the quadrants as shown in Figure 1F and tested their activities in the retina ex vivo. All the active CRMs were in quadrant A2, which contains putative CRMs with higher density of MG-TFBSs (Fig. 1, F and G). When electroporated into mouse retinas in vivo, MG1, MG3, MG6, MG10, and MG12 showed robust and specific activities in MG (Fig. S2, A-F). Interestingly, the active CRMs were not only found flanking MG-enriched genes, but also near bipolar or amacrine-enriched genes (Fig. S1F). No putative CRMs flanking photoreceptor-enriched genes were found to have high density of MG-TFBSs and tested positive in the retina. Overall, these results suggest that the density of MG-TFBSs can be used as a predictor of CRM activity. To further support this conclusion, we generated a synthetic CRM containing TFBSs of 13 MG-specific TFs and found that it can drive MG-specific expression in the retina ex vivo (Fig. S2G).

Integration of a shortened MG1 CRM into AAVs allows targeting of retinal Müller glial cells and brain astrocytes
We tried to integrate the identified MG CRMs into AAVs and test their activity and specificity in the mouse retina in vivo. We started with the MG1 CRM as it showed great specificity and efficiency when tested in reporter plasmids (Fig. S2B). AAV has a packaging limit of 4.7 kb. Shorter CRMs are favored as they can carry larger cargo genes. We first examined whether we could find the minimal element within the MG1 CRM (552 bp) that keeps the specificity and activity. We tested a shortened MG1 CRM (MG1s, 93 bp) containing two MG-TFBSs with the highest JASPAR scores. The higher the score, the higher the similarity between the sequence and TFBS matrix. When cloned into the reporter plasmid Stagia3, the MG1s CRM can drive the expression of GFP and Cre ER specifically in MG in vivo (Fig. S2H). We then integrated MG1s into AAVs and tested its activity and specificity in the mouse retina in vivo ( Fig. 2A). The MG1s CRM showed great specificity (99%) and efficiency (60%) in driving GFP expression in MG when integrated into AAVs (Fig. 2, B, C, F and G). In addition, we used Ai9 Crereporter mouse line, which turns on tdTomato expression upon Cre-mediated recombination, to examine whether MG1s can drive Cre ER expression specifically in MG. The AAV-pMG1s-Cre ER virus can turn on tdTomato expression specifically in MG in the presence of tamoxifen (50 mg/kg, intraperitoneal injection), demonstrating that it is a useful tool for manipulating MG in vivo (Fig. S3).
A few minipromoters that can drive MG-specific expression in AAVs in the retina have been reported previously (8,21,27,28).   The most widely used is GfabC1D (referred to as "GFAP promoter" in this manuscript), which was designed using DNA regions upstream of the Gfap gene (29). However, it is noteworthy that MG upregulate Gfap expression, a hallmark of gliosis, in response to injury and diseases (30)(31)(32)(33)(34)(35). We observed increased activity of the GFAP promoter compared to uninjured controls when we transduced the retina with AAV-pGFAP-GFP and acutely injured the retina with the neurotoxin NMDA ( Fig. 2, D, E and H). The GFAP promoter may therefore lead to uncontrolled expression in disease models. Notably, the MG1s CRM did not respond to NMDA-induced injury (Fig. 2, B, C and H). The MG1s-containing AAV can be useful for targeting and studying MG under pathological conditions. Other reported minipromoters that were used in AAVs to limit the expression in MG were either longer than 2 kb or with relative low labeling efficiency. This limited their usage, especially for carrying large genes such as Cas9 (27). The small size of the MG1s CRM (93 bp) is therefore advantageous. We summarized the specificity and efficiency of AAV-pMG1s-GFP/Cre/Cre ER in targeting MG with different capsids and injection routes in Fig. S4.
In addition, as retinal MGs share a similar gene expression profile with brain astrocytes (23), we also tested the specificity of AAV-pMG1s-GFP/Cre ER in the brain. When injected into the motor cortex (M1) or striatum (STR) of 8-week-old adult wild-type mice, the AAV-pMG1s-GFP virus can label astrocytes with great specificity (>90%) (Fig. 3, A-D). Similarly, the AAV-pMG1s-Cre ER virus can specifically activate Cre-reporter line Ai9 and turn on tdTomato expression in astrocytes in the striatum and motor cortex (Fig. 3, E-H). These AAVs can be useful tools for manipulating astrocytes in the brain.
Synthetic "stitching" CRMs can render cell type specificity in AAVs in the retina For many cell types or subtypes in the retina and brain, the cell-type-specific TF/TFBS profile and chromatin accessibility data may not be readily available. To develop AAVs targeting these cell types, we investigated whether generating a synthetic "CRM" would render cell type specificity. CRMs are highly sensitive to DNaseI. The ENCODE project has published the DNase-seq data on a variety of tissues including the whole retina and brain (11,12). Instead of testing individual DNaseI hypersensitive regions, we tested whether stitching the DNaseI hypersensitive regions flanking a cell-type-specific gene together can render activity and specificity in AAVs.
We first used this strategy to generate AAVs targeting retinal amacrine cells (AC), which are essential interneurons of the visual circuits (36) and with limited molecular tools for labeling and manipulation. We tried to prescreen for candidate AC-or AC-subtype-specific CRMs using AC-enriched TFBSs but failed in that we cannot collect enough AC or AC-subtypeenriched TFs based on the published single-cell RNA-seq data (17). We therefore decided to stitch the DNaseI hypersensitive regions flanking AC-enriched genes and examine whether this strategy can lead to AAVs targeting AC. We focused on three genes that are highly enriched in all AC, or subtypes of AC, including Ndrg4 (N-myc downstream regulated gene 4), Chat (Choline O-sacetyltransferase), and Gad1 (Glutamate decarboxylase 1) (Fig. 4, A-C). Based on the published single cell RNA-seq data and the available literature, Ndrg4 is expressed in all types of retinal AC and some ganglion cells (17) (Fig. S5). A 6.2 kb promoter of the Ndrg4 gene can drive expression specifically in AC via in vivo electroporation (26). Gad1, the enzyme for producing GABA, is a well-known marker for GABAergic AC in the retina. CHAT expressing AC, which is essential for generating direction-selective responses to motion (37), is a subtype of GABAergic AC. For each of these three genes, we collected DNaseI hypersensitive regions flanking the gene in mouse retina at P1, P7, and 8 weeks of age based on the DNase-seq datasets published by the ENCODE project. We prioritized DNaseI hypersensitive regions detected specifically in the retina and rejected those that were simultaneously present in the brain and retina (Fig. 4, A-C). The synthetic CRMs were generated by stitching these DNaseI hypersensitive regions together and cloned into AAV backbones. A modified AAV2 capsid (AAV7m8), previously shown to transduce inner retinal cell types via intravitreal injection (38), was used in combination with engineered AAV backbones.
We injected AAV-pNdrg4-GFP, AAV-pChat-GFP, or AAV-pGad1-GFP into the mouse eyes in vivo at P4 via intravitreal injection (Fig. 4). The P4 stage was chosen in that AAV infection efficiency is much higher at P4 due to relatively small eyeball size at this stage. The retinas were harvest at P21 for histology. The specificity of these AAVs was compared with the control AAV, in which a ubiquitous CAG promoter drives GFP expression. The Ndrg4, Chat, and Gad1 AAVs directed GFP expression specifically in AC in the inner nuclear layer (INL) compared with AAV-pCAG-GFP, as shown by colabeling with a pan-amacrine marker Pax6 (Fig. 4, D-G).
DNA sequence of each putative CRM with MG-TFBSs using JASPAR database. The total number of MG-TFBSs per 100 bp (MG-TFBS density) for each putative CRM was plotted. X axis: the number of MG-TFBSs per 100 bp with JASPAR relative score >10 (the higher the score, the higher similarity between the sequence and JASPAR TFBS matrix). Y axis: the number of MG-TFBSs per 100 bp with JASPAR relative score >11. Red, positive CRMs; Black, negative CRMs. E and F, prescreening of putative CRMs using the hypothesis drawn from the pilot study, which showed that the MG-TFBS density is correlated with CRM activity. E, the MG-enriched genes and control genes (enriched in retinal bipolar, amacrine, and photoreceptor cells) were collected based on single cell RNA-seq data of adult mouse retina (17) and published literature. The putative CRMs flanking these genes were collected based on MG ATAC-seq (18). F, each putative CRM was scanned with MG-TFBSs (B) using JASPAR database. The number of MG-TFBSs per 100 bp was plotted as described in panel D. A1-A4: four quadrants of the plot. We cloned some putative CRMs in quadrant A1, A2 and A3 into reporter plasmid Stagia3 and tested their activities in the retina ex vivo. Light grey dot, untested putative CRMs; Dark gray dot, tested CRMs without any activities in the retina; Red, active CRMs in the retina. Black circle, CRMs failed to be cloned due to high GC contents or repetitive sequences. 1 to 12, MG1 to MG12, the positive CRMs. G, the activities of MG1-12 CRMs in the retina ex vivo were indicated by AP (Alkaline phosphatase) signals. The reporter plasmids containing each CRM were electroporated into P0 mouse retinas together with the electroporation efficiency control plasmid pCAG-mCherry. mCherry: electroporation efficiency control. Dark AP signals covered mCherry signals when CRM activity was strong. N ≥ 4. Scale bar: 50 μm. CRM, cis-regulatory module; MG, Müller glial cell; TFBS, transcription factor binding site. Interestingly, all three AAVs specifically targeted Gad1+ GABAergic AC, despite the expression of Ndrg4 in all types of amacrine cells (Fig. S5B). In ganglion cell layer (GCL), Gad1 AAVs were able to target displaced AC, whereas Ndrg4 and Chat AAVs labeled displaced AC and a percentage of ganglion cells (Fig. S6). The Ndrg4 and Chat AAVs did not label any CHAT positive cells in either INL or GCL, while the Gad1 AAVs labeled fewer CHAT positive AC than the AAV-pCAG-GFP control (Figs. 4G and S6). We also investigated whether the AC-targeting stitching CRMs allow cell-type-specific genetic manipulation when integrated into AAVs. When the Cre gene was inserted into the AC-targeting AAVs, Cre-reporter genes were activated nonspecifically, possibly due to minimal stochastic activity of synthetic CRMs and high efficiency of Cre recombinase (Fig. S7,  A and B). To achieve greater specificity, we generated a modified gCre recombinase by fusing Cre with human geminin protein.

AAV-pGFAP-GFP
Geminin, a key regulator of the cell cycle, is degraded in the G1/ G0 phase of the cell cycle (39). Fusing Cre recombinase with geminin successfully desensitized the Cre activity and reduced nonspecific recombination in postmitotic cells in the retina and brain (Fig. S7). We used the AC-targeting stitching CRMs to drive gCre expression in AAVs, and injected these AAVs into the eyes of Cre-reporter Ai9 mice. pGad1-gCre is too large to fit into the AAV backbone (>4.7 kb). AAV-pChat-gCre and AAV-pNdrg4-gCre activated expression of the tdTomato reporter gene sparsely in AC and some ganglion cells (Fig. 4H). These sparse-labeling AAV tools can be useful for studying the function and circuits of AC at single cell levels. For instance, genetically encoded calcium indicators (GCaMPs) have been widely used to monitor neuronal activity. Coinjection of AAV-pNdrg4-gCre and AAV-DIO-CAG-GCaMP6f (40) (AAV expressing GCaMP6f in a Cre-dependent manner) into wildtype mouse eyes allowed delivery of the GCaMP6f indicator into single AC (Fig. 4I).
Taken together, these results demonstrated that AAVs with synthetic stitching CRMs from AC-enriched genes can label Gad1+ GABAergic AC and provide opportunities for manipulating individual AC in the mouse retina in vivo.

AAVs with synthetic CRMs can target parvalbumin (PV)positive neurons in the brain
PV-positive GABAergic interneuron is one of the major interneuron types in the brain and play important roles in microcircuit regulation. A few AAVs targeting cortical PV+ interneurons have been developed by integrating putative CRMs into AAVs (9, 10). These CRMs were selected based on bulk/scATAC-seq of cortical regions. However, their specificity and efficiency of labeling subcortical PV+ cells were low. The current stitching approach utilizes the DNase-seq data from whole brain (ENCODE project), including both cortical and subcortical regions. This strategy may generate AAVs that can target PV+ interneurons in multiple brain regions.
We identified five putative CRMs (A-E) near the Pvalb gene by investigating the DNaseI hypersensitive peaks of mouse whole brain published by the ENCODE project (Fig. 5A). These putative CRMs were stitched together, placed in front of the PV promoter, and inserted into AAV backbones (AAV-pAllPV-GFP). AAV-pPV-GFP (promoter only, determined based on DNaseI hypersensitivity) and AAV-pBPV-GFP (promoter with CRM B) were also generated to serve as controls. These AAVs with AAV8 capsid were injected into two cortical regions of adult mouse brain, motor cortex (M1) and visual cortex (V1), and one subcortical region, striatum. All three AAVs led to robust GFP expression in the infected regions. Interestingly, we found that the specificity of these AAVs varied across regions (Table S1). The AAV-pAllPV-GFP virus had good specificity (68%) in labeling PV+ neurons in striatum, but the specificity dropped to 35% in motor cortex (Figs. 5B and S8). The AAV-pPV-GFP virus showed a higher specificity in motor cortex (79%) than in striatum (44%) (Figs. 5C and S8). The AAV-pBPV-GFP virus showed the highest specificity in visual cortex (88%) but relatively low specificity in both motor cortex and striatum (40%) (Fig. S8). In addition, we tested whether the synthetic PV CRMs allowed genetic manipulation of PV+ interneurons via Cre recombinase. As we observed in the retina, Cre expression driven by the PV CRMs led to nonspecific labeling in Ai9 reporter mice (Fig. S7, C and D). We therefore placed gCre into the PV AAVs. AAV-pPV-gCre and AAV-pBPV-gCre viruses with AAV8 capsid sparsely labeled PV+ interneurons in M1 and striatum of Ai9 mice with decent specificity (Figs. 5D and S8; Table S1) and could be used to manipulate PV+ interneurons in vivo. AAV-pAllPV-gCre cannot be generated because the size of pAllPV-gCre exceeds the packaging limit of AAVs (>4.7 kb).

Discussion
We explored strategies that can increase the efficiency of identifying CRMs for AAV-based cell-type-specific labeling and manipulation. We showed that prescreening of chromatin accessible putative CRMs based on the density of cell-typeenriched TFBSs can significantly increase the efficiency of identifying active CRMs. In addition, generation of synthetic CRMs by stitching chromatin accessible regions flanking celltype-enriched genes can straightforwardly render cell type specificity in AAVs in many cases. Both strategies utilize available large-scale datasets in the literature and allow generating of AAVs that can target specific CNS cell types without conducting comprehensive screens and large-scale sequencing analyses.
Previous studies have screened for CRMs or synthetic promoters that can target specific cell types when integrated into AAVs in the retina and brain. The screen reported by Jüttner et al.     single-cell ATAC-seq of mouse brain tissues and utilized the data to nominate CRMs for AAVs (10,41). This strategy improved the efficiency of identifying active CRMs, but it highly relies on the success of single-cell ATAC-seq analyses, which could be relatively complicated to perform, especially for low-abundant cell types. Certain chromatin accessible regions in the genome also may not be revealed by single-cell ATAC-seq because of the depth of single-cell sequencing.
The major strength of the prescreening strategy is that it they can increase the efficiency of identifying cell-type-specific CRMs using available genomic data. The results of our analysis suggest that putative CRMs containing >1 (JASPAR score > 11) or >1.7 (JASPAR score > 10) MG-TFBSs per 100 bp are likely active CRMs in MG. (Fig. 1F). The major strength of the stitching strategy is that it is easy to apply and highly practical, especially for cell types with limited information on chromatin accessibility. Both strategies have limitations and can be improved. We formulated and tested the prescreening strategy using retinal MG as a model system. It would be important to apply this strategy to other retinal or brain cell types as more cell-typespecific ATAC-seq, scATAC-seq, and RNA-seq data become available. In addition, we weighted all the MG-enriched TFBSs equally in this study because it is difficult to predict the power and activity of TFs in retinal cells in vivo. Weighting TFBSs differentially based on their affinity and activity is probably necessary for better prediction. The stitching strategy can restrict gene expression to desired neuronal cell types and has comparable efficiency to other published strategies (Table S1). But AAVs generated by this strategy may fail to fully recapitulate the endogenous gene expression patterns. For example, the stitched CRMs designed based on the chromatin open regions flanking the Ndrg4, Gad1, and Chat genes all favored the labeling of Gad1+Chat-cells in AAVs and failed to mark any Chat+ cells in the retina. This is not due to the accessibility of the AAV7m8 capsid that we used, because control AAV-pCAG-GFP with AAV7m8 capsid can label Chat+ cells. One possibility, which needs to be further investigated, is that chromatin accessible silencer CRMs may be included in these stitched CRMs.
Apart from identifying active CRMs for AAVs, the prescreening and stitching strategies also allow for revealing of unknown biology regarding gene regulation. Using the prescreening strategy, we found that the putative CRMs with high density of MG-enriched TFBSs were not only found near MG-enriched genes, but also present near bipolar and amacrine-enriched genes. For example, the MG1 CRM with high density of MG-enriched TFBSs resides in the intron of the Trmp1 gene, which is well known to be expressed in retinal bipolar cells but not in MG. Therefore, some bipolar or amacrine-enriched genes cannot be transcribed in MG even though they are near the CRMs that can be activated in MG. Additional mechanisms may be needed to repress the expression of bipolar or amacrineenriched genes in MG. In the brain, we tested and compared multiple chromatin accessible putative CRMs flanking the Pvalb gene by stitching them together in AAVs. PV promoter and CRM B showed good labeling of cortical PV+ neurons (M1 and V1). Adding CRM A, C, D, and E regions led to better targeting of subcortical PV+ neurons, indicating that there may be putative striatum-specific CRMs among these CRMs (Fig. 4).
In addition, we introduced a new tool to the field. The previously reported cell-type-specific synthetic promoters or CRMs often led to nonspecific labeling when driving the expression of Cre or destabilized Cre recombinase (Fig. S8). The gCre protein generated here can significantly reduce the sensitivity of Cre and increase the gene manipulation specificity mediated by Cre recombinase in AAVs.
In summary, we tested two straightforward strategies for increasing the efficiency of identifying cell-type-specific CRMs for AAVs and generated useful AAV tools for targeting retinal MG and AC and brain astrocytes and PV+ neurons. Application of these strategies in other CNS cell types could significantly improve the efficiency of generating AAV tools targeting specific cell types.

Animals
Wild-type mouse neonates were obtained from time pregnant CD1 mice (Charles River Laboratories, #022). C57BL/6J (Stock No: 000664) and Ai9 (Stock No: 007909) mice were purchased from the Jackson Lab. All animal studies were approved by the Administrative Panel on Laboratory Animal Care (APLAC) at Stanford University.

Plasmid construction
The CAG-mCherry plasmid was from Matsuda and Cepko (42). The Stagia3 vector was from Billings et al. (24). The AAV-GFAP-GFP plasmid was a gift from Dr Wenjun Xiong (City College of Hong Kong). All other plasmids were constructed by restrictive enzyme-based cloning.
CRM reporter plasmids (Fig. 1) The putative CRMs were amplified from the mouse genome by PCR and cloned into the Stagia3 vector. The sequences of putative CRMs and promoters were listed in Supplementary File 1.

AAV plasmids (Figs. 2-5)
The AAV-pMG1s-GFP plasmid was generated by replacing the GFAP promoter of the AAV-pGFAP-GFP plasmid with MG1s. The AAV-pMG1s-CreER and AAV-pGFAP-CreER plasmid were generated by replacing the GFP of the AAV-pMG1s-GFP and AAV-pGFAP-GFP plasmids with CreER (Addgene #14797) (26). The Ndrg4, Gad1, and Chat synthetic CRMs were synthesized by IDT and cloned into AAV vectors. gCre was generated by fusing the human Geminin protein (amino acid 1-110) to the C terminus of Cre recombinase. AAV-Ndrg4/Chat-gCre was generated by replacing the GFP of the AAV-Ndrg4/Chat-GFP plasmid with gCre. PV CRMs were PCR amplified from mouse genomic DNA, stitched by Gibson assembly (NEB, Cat.No. E2611), and cloned into AAV backbones. The sequences of synthetic CRMs and gCre were listed in Supplementary File 1.

AAV production and delivery
Recombinant AAV8, AAV7m8, and AAVshH10 viruses were produced as previously described (43). The AAV8 and AAV7m8 capsid plasmids were gifts from Dr Wenjun Xiong and Dr Connie Cepko (Harvard Medical School). The shH10 capsid was a gift from Drs John Flannery & David Schaffer (Addgene plasmid # 64867). SDS-PAGE gels were used to determine the titer of AAVs.
Viruses were delivered to adult brains by stereotaxic injection. Briefly, mice were anesthetized by 1.5 to 2.0% isoflurane and placed on a stereotaxic frame (Kopf instrument). A glass needle was prepared by micropipette puller, filled with virus solution, and fixed on the micromanipulator. Then, the needle was moved to the desired position over a predrilled hole in the skull and slowly lowered to the target depth. In total, 0.5 μl of virus solution was injected at an infusion rate of 50 to 100 nl/min. Following virus injection, the scalp was sutured, and mice were returned to their home cages for 2 weeks before tissue harvesting. To locate specific areas, the following coordinates were used: M1, AP: 1 mm, ML: ±1.5 mm from bregma, DV: −1.0 mm from brain surface; STR, AP: 1.2 mm, ML: ±2 mm from bregma, DV: −2.2 mm from brain surface; V1, AP: −2.8 mm, ML: ±2.2 mm, DV: −1.5 mm from bregma.

Ex vivo and in vivo plasmid electroporation into the retina
Ex vivo and in vivo retina electroporations were carried out as previously described (25,26,42). For ex vivo electroporation, five pulses of 25 V, 50 ms each, and 950 ms intervals were applied to dissected retinas. For in vivo electroporation, five pulses of 80 V, 50 ms each, and 950 ms intervals were applied to neonatal mouse pups. All ex vivo and in vivo electroporation experiments were repeated with at least three biological replicates. Plasmids were electroporated with a concentration of 500 ng/μl to 1 μg/μl per plasmid.

Histology and immunohistochemistry
Dissected mouse eyes were fixed in 4% paraformaldehyde (PFA) in PBS (pH 7.4) for 2 h at room temperature. Retinas then were dissected and equilibrated at room temperature in a series of sucrose solutions (5% sucrose in PBS, 5 min; 15% sucrose in PBS, 15 min; 30% sucrose in PBS, 1 h; 1:1 mixed solution of OCT) and 30% sucrose in PBS, 4 C, overnight, frozen and stored at −80 C. A Leica CM3050S cryostat (Leica Microsystems) was used to prepared 20 μm cryosections. Retinal cryo-sections were washed in PBS briefly, incubated in 0.2% Triton X-100 (Sigma-Aldrich), PBS for 20 min, and blocked for 30 min in blocking solution, which is 0.1% Triton, 1% BSA, and 10% Donkey Serum (Jackson ImmunoResearch Laboratories, Inc) in PBS. Slides were incubated with primary antibodies diluted in blocking solution in a humidified chamber at room temperature for 2 h or at 4 C overnight. After washing in 0.1% Triton PBS three times, slides were incubated with secondary antibodies and DAPI (Sigma-Aldrich; D9542) for 30 min to 2 h, washed three times with 0.1% Triton PBS, and mounted in Fluoromount-G (Southern Biotechnology Associates).
To harvest the brains, mice were anesthetized with isoflurane and transcardially perfused with PBS followed by 4% PFA/PBS. Brains were collected, post-fixed in 4% PFA/PBS for 4 h, and dehydrated in 30% sucrose/PBS for 2 days at 4 C. Brains were sectioned into 30 μm slices by a cryotome or cryostat (Leica Microsystems). Floating slices were blocked with PBS containing 10% Donkey Serum and 0.5% Triton X-100 at room temperature for 2 h. Then the slices were incubated with primary antibodies and second antibodies (as described above) and mounted in VECTA-SHIELD(R) Mounting Medium with DAPI (Vector Laboratories, H1500).

Imaging and analysis
All images of retinal and brain sections were acquired by a Zeiss LSM880 inverted confocal microscope. Retina explants were imaged by Leica M165 FC microscope. Images in Figures 2 and 4 were maximum projections of 5 to 10 μm tissues and were quantified by Fiji software. Images in Figures 3 and 5 were projections of 30 μm tissues.
Collection of cell-type-enriched genes/TFs, putative CRMs, and TFBS density analyses MG-enriched TFs (Fig. 1B) and MG, BP, AC, or PRenriched genes (Fig. 1E) were collected based on available single-cell RNA-seq (scRNA-seq) data on adult mouse retinas (17) and published literature (23). The scRNA-seq gene expression matrix was downloaded from GSE63472, and the computational analysis was performed by using the SEURAT2 pipeline. The cells that contained less than 20% mitochondrial reads and more than 900 genes per cell were kept for downstream analyses, which included the standard functions Nor-malizeData, FindVariableGenes, ScaleData, RunPCA, FindClusters (resolution of 0.6), RunTSNE, and FindAll-Markers. The criteria for selecting cell-type-enriched genes and TFs were as follows (using MG-enriched genes/TFs as examples). First, we selected the genes that are significantly enriched in MG cluster in the scRNA-seq data. Specifically, the Seurat "FindAllMarkers" function with default parameters (object = pbmc, only.pos = FALSE, min.pct = 0.25, thresh.use = 0.25) was used to identify marker genes enriched in MG cluster. The Wilcoxon Rank Sum test was used to generate p values. The average expression level of each marker gene in MG cluster versus all other cell clusters was calculated as Log 2 (FC) (the log-ratio of fold change). We considered genes with Log 2 (FC) >2 and p value <0.05 as MG-enriched genes. Among these MG-enriched genes, we selected genes encoding transcription factors as MG-enriched TFs. The scripts for these analyses are available at: https://github.com/tanasa/the_ scripts_analysis_scRNAseq.
To collect putative CRMs (Fig. 1), we downloaded the BigWig files for DNase-seq of mouse retina tissue from the EDITORS' PICK: Identification of cell-type-specific CRMs for AAVs ENCODE portal (https://doi.org/10.1093/nar/gkx1081) (https://www.encodeproject.org/) (12) and raw ATAC-seq data for MG from NCBI Sequence Read Archive (accession SRX2881310) (18). The raw ATAC-seq data were analyzed using the Galaxy platform (usagalaxy.org) following standard pipelines. Specifically, reads were aligned to mouse mm9 genome using Bowtie2 (44) and processed with MarkDuplicates to locate duplicate reads. MACS2 callpeak function was used to generate with narrow peaks. The Wig/BedGraph-to-bigWig function with default parameters was used to generate BigWig coverage tracks, which were visualized in UCSC genome browser. We selected putative CRMs (peaks in ATAC-seq or DNase-seq data) using the following criteria manually. (1) We scanned the regions flanking a gene of interest until the neighboring genes (UCSC genes, mouse mm9) in BigWig coverage tracks. If an ATAC peak overlapped with an exon, we excluded it from the selection. (2) We collected peaks with the tip signal values ≥20. The left or right boundary of the peak (putative CRM) was set when the signal value reached 20% of the tip signal value. All the sequences of the selected putative CRMs/peaks were listed in Supplementary File 2.
The TFBS matrices of MG-enriched TFs were obtained from JASPAR database (jasper.genereg.net) (45), and manually imported into JASPAR website. We used the "SCAN" function (Relative profile score threshold 80%) in JASPAR website to predict TFBSs within each putative CRM. The TFBSs with JASPAR score >10 or >11 were collected. If the start of a TFBS is within 5 bp from the start of another TFBS, we considered them as 1 "TFBS count." The number of "TFBS count" was calculated for each putative CRM. The MG-TFBS per 100 bp (Fig. 1F) is "TFBS count" divided by the length of CRM (bp) *100. We wrote a python program to automatically count TFBSs and calculate the number of "TFBS count" per 100 bp (Supplementary File 2). If a CRM contains long tandem repeats (such as CTCCCT or ACACACACACACAC), we manually correct the data by skipping the repeated region.

Data availability
All data are contained within the manuscript.
Supporting information-This article contains supporting information.


Lysophosphatidic acid supports the development of vitrified ovarian follicles by decreasing the incidence of cell death: An experimental study

Abstract Background Lysophosphatidic acid (LPA) contributes to follicular activation, oocyte maturation, in vitro fertilization, and embryo implantation. Objective This study was designed to evaluate the effects of LPA to improve the development of isolated follicles derived from whole mouse cultured vitrified ovaries. Materials and Methods In this experimental study, first, the 1-wk-old mouse ovaries in the non-vitrified and vitrified groups were cultured in the presence of 20 µM of LPA for 1 wk. Then, their isolated preantral follicles were cultured individually for 12 days in the presence or absence of 40 µM of LPA. The following evaluations were done for the cultured follicles: a viability test using Calcein AM staining, flow cytometry using annexin V/Pi, and analysis of the expression of genes by real-time reverse transcription polymerase chain reaction. The maturation rates of the oocytes were compared among groups and some of the released metaphase II oocytes were subjected to in vitro fertilization. Results In all LPA treated groups, the rates of survival and follicular development were higher, and the incidence of cell death and expression of pro-apoptotic genes were lower, than in the non-LPA supplemented groups (p = 0.035). There was no significant difference between the vitrified and non-vitrified groups regarding follicular or oocyte development, but the expression of Bad and LPA receptors genes was significantly altered in the vitrified LPA supplemented group in comparison with the non-vitrified LPA supplemented group (p = 0.028). Conclusion LPA improved the survival and developmental potential of the isolated follicles. Despite some alterations in the expression of apoptosis-related genes in the vitrified ovaries, LPA had positive effects on the survival and development of these follicles.

Introduction
The cryopreservation and in vitro culture of ovaries and isolated preantral follicles have been introduced as an alternative approach for fertility preservation. Several factors such as oxidative stress, hypoxia, and temperature alterations may influence the survival and development of ovarian follicles during in vitro growth and many attempts have focused on improving these procedures (1)(2)(3). Moreover, in the literature, it has been shown that cell death may be involved in follicular damage and degeneration following ovarian cryopreservation and in vitro culture (2,(3)(4)(5)(6)(7). Some advances have been achieved in the optimization of in vitro culture of follicles by using certain growth factors, antioxidants, and anti-apoptotic factors (3,4,8,9).
Lysophosphatidic acid (LPA) is a natural transmembrane lysophospholipid with 430-480 Da molecular weight, and it has growth factor-like and hormone-like activities (10). LPA contributes to the regulation of female reproductive functions via autocrine and paracrine manners such as follicular activation, oocyte nuclear and cytoplasmic maturation, in vitro fertilization, and embryo implantation (10)(11)(12). LPA is present in human follicular fluid and it is produced by various cell types such as the cumulus and theca cells (13,14). LPA carries out its biological functions through at least 6 high-affinity, transmembrane G-proteincoupled receptors (LPAR1-6) that are controlled by different genes in human, bovine, ovine, porcine, and rodent species (10,(15)(16)(17)(18)(19). LPARs have been found in reproductive tissues including the uterus and ovary (20), and progesterone and estradiol regulate LPAR expression (21,22).
Several reports have revealed the beneficial effects of LPA supplementation with a range of concentrations between 10-30 µM/L in culture media to improve oocyte maturation and fertilization and subsequent embryo development in bovine, mouse, and pig species (23)(24)(25)(26)(27)(28).
Recently, in our previous studies, we showed that 20 µM of LPA had positive effects on cell survival and function of whole mouse ovaries during the cultivation period (29,30). It was demonstrated that LPA, as a survival factor, enhanced the follicular development after transplantation of mouse ovaries by changing the pattern of the anti-and pro-apoptotic genes in association with miR-22 expression (31). Also, in another study revealed that the addition of LPA to culture media of human ovarian tissue improved the follicular survival and development by changing the ratio of BCL2 and BAX expression as apoptotic-related genes (32).

Animal and ovaries collection
The ovaries were obtained from 1-wk-old National Medical Research Institute (NMRI) female mice that were kept and bred under a 12 hr light/dark cycle at 22 ± 2°C and 40-50% humidity at Tarbiat Modares University animal house, Tehran, Iran. The mice (n = 20) were killed by cervical dislocation, and their ovaries were isolated and dissected free of fat and mesentery and placed into an alpha-minimal essential medium (α-MEM; Invitrogen, Paifley, UK), which was supplemented with 5% fetal bovine serum (FBS; Invitrogen, Paifley, UK), 100 IU/ml of penicillin (Invitrogen, Paifley, UK), 100 µg/ml of streptomycin (Invitrogen, Paifley, UK), 0.23 mM of sodium pyruvate (Sigma Aldrich, Dusseldorf, Germany) and 0.22 gr/ml of sodium bicarbonate (Sigma Aldrich, Dusseldorf, Germany).

Experimental design
This experimental study was designed in 2 steps. In the first step, the collected whole ovaries were cultured in the presence of 20 µM of LPA for one wk (29). Then, their preantral follicles were mechanically isolated and cultured individually for 12 days in the presence of different concentrations of LPA (0, 10, 20, 40, 80 µM). The group with the highest survival and developmental rates of follicles and maturation rate of oocytes was selected for the second step of this study. In the second step, the whole ovaries were collected again and some of them were vitrified (33) and after one wk, in vitro culture as a control group, their preantral follicles were obtained, capsulated and cultured for 12 days in the presence or absence of LPA (the concentration of LPA was selected according to the results of the first step of the study). Thus, the groups of the study included non-vitrified LPA − , non-vitrified LPA + , vitrified LPA − and vitrified LPA + . The following evaluations were done for the cultured follicles in all studied groups: a viability test using Calcein AM staining, cell death analysis by flow cytometry using an annexin V/Pi kit, analysis of the expression of genes related to apoptosis, in vitro fertilization, and embryonic development.

The first step of the study 2.3.1. Ovarian culture and follicular isolation
The whole ovaries (n = 10 in each group) in the non-vitrified groups were cultured on inserts (Millicell-CM, 0.4-m pore size; Millipore Corp, Billerica, MA, USA) in 24-well plates at 37°C, 5% CO 2 for 7 days. Each well contained 400 µl of α-MEM culture medium containing 5% FBS, 1% insulin, transferrin, and selenium (ITS; Invitrogen, Paifley, UK), 100 mIU/ml of recombinant follicle stimulating hormone (rFSH or Gonal-f; Serono, Switzerland) and 20 µM of LPA (1-oleoyl-2-hydroxysn-glycerol-3-phosphate sodium salt; Avanti Polar Lipids, Instruchemie, The Netherlands). Every other day, 200 µl of the culture media in each well was refreshed. At the end of the culture period, by insulin-gauge needles, the preantral follicles (140-150 µm in diameter) were manually isolated (n = 50 in each group). The criteria for follicle isolation were 2-3 layers of granulosa cells, healthy and visible oocytes, and a thin layer of theca cells.

Encapsulation of follicles in the sodium alginate and 3-dimensional in vitro culture
The isolated preantral follicles (n = 50 in each group, repeated 5 times) were encapsulated in sodium alginate using the method described previously (34). The sodium alginate solution 0.5% (w/v) was reconstituted with sterile phosphate International Journal of Reproductive BioMedicine Abedpour et al. buffer saline (PBS). The collected follicles were individually plunged into 5 µl droplets of sodium alginate; then they were put into 50 mM of CaCl 2 and 140 mM of NaCl for 2 min, and then washed in α-MEM media. After that, these follicles were cultured in the medium containing 0, 10, 20, 40, or 80 µM of LPA.

Assessment of follicular diameter
Under an inverted microscope the morphology of the cultured follicles was observed every 48 hr. The normal follicles had a clear appearance and the degenerated follicles had dark and fragmented oocytes and granulosa cells (34). The diameter of the follicles was measured using a pre-calibrated ocular micrometer at x100 magnification under an inverted microscope (n = 45/each).

In vitro ovulation and induction
After 12 days of in vitro culture of follicles, ovulation was induced by adding 1.5 IU/ml human chorionic gonadotropin hormone (Organon, Griekenweg, Netherlands) to the culture media (n = 50 in each group, with 5 repeats). Then, the released oocytes were classified as a germinal vesicle, germinal vesicle breakdown, or metaphase II (MII).

The second step of the study
In the second step of this study, the whole ovaries were collected again and divided randomly into non-vitrified and vitrified groups (n = 15/ each).

Vitrification and warming procedures
The ovaries (n = 15) were vitrified and warmed based on the previously described protocol with some modifications (33). The ovaries were dehydrated with a vitrification medium (EFS40) containing 40% ethylene glycol (v/v), 30% ficoll 70 (w/v), and 1 M sucrose for 5 min at room temperature. Afterward, they were individually put on the CryoLock (Biotech, UK) with a minimum volume of the vitrification solution and were quickly plunged into liquid nitrogen and stored for 7 days. For warming, the CryoLocks were immersed into 1000 µl of 1 M sucrose solution at 37°C and were sequentially transferred in descending concentrations of sucrose (0.5 and 0.25 M) for 5 min (in each step) at room temperature. Warmed ovaries were equilibrated in an α-MEM medium supplemented with 5% FBS for one hr at 37°C in humidified 5% CO 2 air before tissue culture and evaluation.

3-dimensional in vitro culture of isolated follicles in vitrified and nonvitrified groups
The non-vitrified and vitrified/warmed ovaries were cultured (n = 15 in each group) for one wk, and then their preantral follicles were mechanically isolated (n = 380 follicles in each group), encapsulated, and cultured in the same way as described for the first step of the study. The encapsulated follicles were individually cultured (n = 340 follicles in each group) in an α-MEM medium supplemented with 5% FBS, 100 mIU/ml recombinant FSH, 1% ITS, 100 mg/ml penicillin, and 50 ng/ml streptomycin in the presence and absence of 40 µM of LPA (the concentration of LPA was selected according to the results of the first step of the study) under mineral oil at 37°C with 5% CO 2 for 12 days. These experiments were done at least 10 times. Every other day, 200 µl of the culture medium in each well was refreshed and the remaining media were stored separately at -20 C for hormonal evaluation (34). Ovulation was induced after 12 days of in vitro culture of follicles by adding 1.5 IU/ml human chorionic gonadotropin hormone to the culture media (n = 50 in each group, with 5 repeats). Then the oocytes were evaluated and the MII oocytes were subjected to in vitro fertilization.

Evaluation of the ovarian follicular viability using Calcein AM staining
For evaluating the survival rate of the isolated follicles in the vitrified and non-vitrified groups, the isolated preantral follicles (n = 20 in each group, with 3 repeats) were stained with double fluorescent labeling dyes with Calcein AM and ethidium homodimer according to the Live/Dead Viability kit (Invitrogen, UK). The isolated preantral follicles were washed with PBS and then incubated in 1.6 µM of Calcein AM and 5.5 µM of ethidium homodimer for 30-45 min at 37°C in the dark. Then, follicles were reported as survived (stained green), dead (stained red), or damaged (stained partially green and red) under a fluorescent microscope.

In vitro fertilization and embryo culture
The sperm suspensions obtained from the cauda epididymis (7-8-wk-old male NMRI mice) were placed into a 500 ml drop of global medium (Life Global, UK) that was supplemented with 5 mg/ml bovine serum albumin (BSA) under mineral oil and were incubated at 37ºC in humidified 5% CO 2 air for 1.5 hr. The collected MII oocytes derived from all groups (n = 25 for each group) were inseminated with capacitated spermatozoa at a final concentration of 1 × 10 6 /ml in a global medium that was supplemented with 15 mg/ml BSA for 4-6 hr. Then, the oocytes were washed and cultured in global media supplemented with 5 mg/ml BSA under mineral oil at 37ºC and 5% CO 2 . Fertilization and development of embryos were evaluated up to 120 hr later.

Flow cytometry
The flow cytometry evaluation was performed to distinguish the intact, early, and late apoptotic and necrotic cells in the cultured follicles in the vitrified and non-vitrified groups. The samples were obtained from the ovarian follicles on day 12 of culture (n = 90 follicles in each group, with 3 repeats). The follicular cells were obtained by pipetting and then adding to 0.25% trypsin/0.04% EDTA in PBS, then they were washed with 2% BSA. The cell suspension (10 6 cells/ml) was filtered through a 100-μm nylon mesh cell strainer and incubated for 10 min. Then it was washed with 2 ml of warm PBS 2 times and was incubated for 15 min in annexin V-fluorescein isothiocyanate and propidium iodide staining solution, in accordance with the instructions of a commercial assay kit (annexin V-FITC Apoptosis Detection Kit, Biotool, UK) and finally, the binding buffer was added. Using this technique, intact, early apoptotic, late apoptotic and necrotic cells showed no fluorescence, green, orange, and red fluorescence, respectively. The data of flow cytometry were assessed using flowJo software (Life Sciences, Ashland, Oregon).

RNA extraction
Total RNA was extracted from the cultured ovarian follicles in all groups at the end of 12 days of culture (n = 30 follicles in each group, with 3 repeats) using the trizol reagent, in accordance with the manufacturer's instructions (RNeasy Mini Kit; Qiagen, Germany). The ovarian follicles were homogenized with 0.5 ml of trizol reagent, then 0.1 ml of chloroform was added and the samples were centrifuged at 12,000 g for 10 min. The upper colorless aqueous phase was removed to a fresh 2 ml microtube and 700 μl of 100% isopropanol was added and centrifuged at 12,000 g for 10 min. Then 1.5 ml of 70% ethanol per 1 ml of trizol reagent was added. Using spectrophotometry (Eppendorf, Hamburg, Germany) RNA concentration was determined. The cDNA was synthesized from RNA by the cDNA kit (Thermo Fisher Scientific, UK), in accordance with the manufacturer's instructions, which was then stored at -20ºC until the real-time reverse transcription polymerase chain reaction (RT-PCR).

Real-time RT-PCR
The primers for LPAR1-4, BAX, Bad, BCL 2 , P53, GDF9, and BMP15 were designed using the online software package Gen Bank database (http://www.ncbi.nlm.nih.gov) ( Table I) and were assessed using the IDT Primer Quest tool -Oligo Analyzer (http://scitools.idtdna.com/analyzer/ Applications/OligoAnalyzer). For BLAST analysis, the NCBI database (http://www.ncbi.nlm.nih.gov/ tools/primer-blast/index) was used. Real-time RT-PCR was done using the QuantiTect SYBR Green RT-PCR kit (Applied Biosystem, USA) Each PCR reaction tube (20 μl) contained 2 μl of cDNA product, 2 μl each of forward and reverse primers, 6 μl of water, and 10 μl of SYBR Green. The real-time RT-PCR protocol was performed as the denaturation stage for 5 min (at 95ºC), cycling stage of denaturation for 15 sec (at 95ºC), annealing stage for 30 sec (at 58ºC), melting stage for 15 sec (at 72ºC), and a melt curve stage at 95ºC for 15 sec, 60ºC for one min, and 95ºC for 15 sec. To determine the relative quantity of the target genes, the Pfaffl method was used.

Statistical analysis
Data analysis was done using the Statistical Package for the Social Sciences software (V24; SPSS Inc., Chicago, IL, USA). Values were given as mean ± SD. The results of the survival and developmental rates of the cultured follicles and embryos, the diameter of the cultured follicles, flow cytometry analysis, and mRNA expression in all studied groups were compared with one-way ANOVA, and Tukey's HSD was used as post hoc tests. Statistical significance was considered as a p-value of less than 0.05 at the 95% confidence level.

The morphology of cultured ovaries
The light microscopy of mouse whole ovaries in all studied groups are presented in figure 1. The size of the ovaries increased during the cultivation period. The follicles exhibited outgrowth in the cortical parts of the ovaries.

The growth and developmental rates of cultured follicles in the presence of different concentrations of LPA (first step)
The diameter and growth of the follicles cultured in different concentrations of LPA were compared within groups. The diameter of the follicles of the group with 0 µM of LPA increased from 149.56 ± 1.7 µm at the beginning of the culture period to 343.73 ± 2.4 µm at the end of the culture period. The diameter increased from 147.55 ± 1.3 µm to 358.13 ± 2.8 µm in the 5 µM LPA group, from 150.15 ± 1.4 µm to 358.25 ± 2.6 µm in the 10 µM LPA group, from 149.25 ± 1.9 µm to 368.53 ± 2.5 µm in the 20 µM LPA group, from 148.57 ± 1.8 µm to 415.85 ± 2.7 µm in the 40 µM LPA group and from 145.53 ± 1.5 µm to 372.23 ± 2.5 µm in the 80 µM LPA group (p = 0.025). In the 40 µM LPA supplemented group, the follicles had a significantly larger diameter than other groups (p = 0.025). The survival rates, antrum formation, and oocyte maturation in the isolated follicles cultured with different concentrations of LPA are summarized in table II. The highest rate of follicular survival, antrum formation, and oocyte maturation (% of MII oocytes) was achieved in the 40 µM LPA treated group (p = 0.035). Thus, the 40 µM of LPA group was selected for supplementation of culture media in the subsequent experimental groups.

The survival rate and phasecontrast microscopy observation of isolated preantral follicles using Calcein AM staining in vitrified and non-vitrified groups
After the mechanical isolation of follicles from cultured ovaries, these follicles were stained by Calcein AM. The representative micrographs of survived (green), degenerated (red), and damaged (green and red) follicles are presented in figure 2 A-C. The phase-contrast morphological observations of the cultured isolated follicles in the studied groups are shown in figure 2 D-K. The size of the follicles increased during the culture period and a clear antrum formed within these follicles (white arrow). The percentages of survived, degenerated and damaged follicles were not significantly different across the studied groups (

Diameter of cultured preantral follicles in the vitrified and nonvitrified groups
The diameter of the follicles in all groups increased during the culture period (Figure 2 M).

International Journal of Reproductive BioMedicine
Abedpour et al.
The diameters of the follicles at the end of the culture period in the non-vitrified LPA − , non-vitrified LPA + , vitrified LPA − and vitrified LPA + were 358.63 ± 2.60, 418.15 ± 2.35, 335.31 ± 2.31, and 387.62 ± 2.74 µm, respectively. There was no significant difference in the diameter of follicles between the non-vitrified and vitrified groups, but this parameter was significantly higher in the 2 LPA supplemented groups than in their respective non-LPA treated groups (p = 0.035).

Developmental rates of cultured preantral follicles in vitrified and nonvitrified groups
After 12 days of in vitro culture, the percentages of survived follicles, the rates of antrum formation, and the maturation rates of oocytes were compared in the vitrified and non-vitrified groups in the presence or absence of LPA and the results are presented in table III. A significantly higher percentage of survived follicles, antrum formation, and MII oocytes was observed in both of the LPA treated groups in comparison with their respective non-LPA treated samples (p = 0.035), but there was no significant difference between the vitrified and non-vitrified groups.

Fertilization rate and embryo development in studied groups
The rat of MII oocytes that fertilized in the non-vitrified LPA − , vitrified LPA − , non-vitrified LPA + and vitrified LPA + groups were 78.57 ± 2.56%, 70.07 ± 5.01%, 82.87 ± 6.85% and 77.33 ± 5.43%, respectively (Table IV). The hatching rates of embryos in the previously listed groups were 31.81 ± 6.35, 26.31 ± 8.03, 44.82 ± 5.13, and 32.01 ± 6.31, respectively. There was no significant difference between the vitrified and non-vitrified groups in these regards, but the fertilization and hatching rates were significantly higher in the 2 LPA treated groups than in their respective non-treated groups (Table V, p = 0.028).

Flow cytometry
The results of the flow cytometry analysis showed that there was no significant difference in the proportion of intact, apoptotic, and necrotic cells in the cultured follicles of the vitrified vs. non-vitrified groups. Moreover, in all LPA treated groups, the percentage of intact cells was significantly higher and the percentage of early and late apoptotic and necrotic cells was lower than in the non-LPA treated groups (Table V, p = 0.035).

Real-time RT-PCR analysis
The expression ratio of the LPAR1-4 genes to the -actin gene in the studied groups was demonstrated and compared in figure 3 A-D. These ratios were significantly higher in all LPA treated groups in comparison with the non-treated groups (p = 0.035). In addition, this expression ratio was significantly lower in the vitrified groups than in the non-vitrified groups (p = 0.028). The mRNA levels of GDF9 and BMP15 in the vitrified and non-vitrified groups in the presence or absence of LPA were compared and shown in figure 3 E-F. The expression of the GDF9 and BMP15 genes was significantly higher in the groups that were cultured in the presence of LPA in comparison with those cultured in the absence of LPA, but there was no significant difference between the vitrified and non-vitrified groups. The expression ratio of the apoptoticrelated genes including Bax, Bcl2, P53, and Bad in the non-vitrified and vitrified groups in the LPA supplemented and non-supplemented groups were compared and demonstrated in figure 4. The expression ratios of the pro-apoptotic genes Bax and Bad were significantly lower in the LPA supplemented groups in comparison with the non-LPA supplemented groups (p = 0.035). Moreover, there was a significant difference between the vitrified and non-vitrified groups in the presence of LPA (p = 0.028). In addition, there was a significantly lower ratio of BCL2/BAX expression in the vitrified group in comparison with the non-vitrified group (p = 0.028).    Data are presented as Mean% ± SD. Significant differences with respective lysophosphatidic acid (LPA) − group (p = 0.035), Significant differences with respective non-vitrified follicles (p = 0.038)

Discussion
In the present study, we examined whether LPA supplementation of culture media could improve the mouse ovarian follicular in vitro growth and development. First, we attempted to determine the most appropriate concentration of LPA for improving the in vitro maturation condition of isolated follicles. In the present study, for the first time, we revealed that among different examined concentrations of LPA, a 40 µM concentration of LPA could enhance the development and growth of isolated follicles up to the antral stage. According to our results, the follicular growth and development gradually increased as the concentration of LPA raised to 40 µM, but these rates were lower at a concentration of 80 µM of LPA.
Results obtained from other parts of this study demonstrated that LPA with 40 µM concentration had more positive effects on mouse follicular development and also on the oocyte maturation and subsequent embryo development in comparison with the non-LPA supplemented control groups. The selected concentrations of LPA in the present and previous studies (29,30) were similar to the level of this molecule in the follicular fluid (13). Several other studies have published similar results on the effects of LPA on the in vitro maturation of oocytes in bovine and porcine species, and in the golden hamster (23)(24)(25)(26)(27)(28). These studies considered a range of applied concentrations of LPA of 10-30 µM/L and our results are in line with their observations (23)(24)(25)(26)(27)(28). Our molecular analysis showed that the expression of 2 follicular developmental genes (Gdf9 and Bmp15) was significantly higher in the LPA treated groups than in the non-treated groups. It has been demonstrated that Gdf9 and Bmp15 are expressed in oocytes of all follicular stages (except primordial follicles) and these genes contribute to the regulation of female reproductive functions (22,35).
In agreement with our study, another research showed that LPA can enhance the expression of several genes related to bovine oocyte pluripotency (OCT4 and SOX2) and developmental competence (follistatin and Gdf9); they also showed that LPA reduced the expression of cysteine proteinase, an enzyme which impairs the developmental potential of bovine oocytes (24).
Our results demonstrated that 4 types of LPARs (1-4) were expressed at the mRNA level in the cultured mouse follicles. Interestingly the level of LPA receptor mRNA was higher in the LPA supplemented groups. It seems that LPA may play autocrine and paracrine roles in follicular cells and oocytes. Also, related reports have confirmed that LPARs can modulate autocrine and/or paracrine functions and promote nuclear and cytoplasmic maturation of mouse oocytes (23,24,36).
Our results also showed that the follicular diameter was significantly higher in all LPA treated groups than in the non-treated control groups. The increase in the size of follicles could be related to proliferation and expansion of follicular and theca cells and it may be due to the growth factor-like and hormone-like activities of LPA in these cells (13). Similarly, the proliferative effect of LPA on other cells has been demonstrated in previous studies via stimulating glucose metabolism (23,24,37).
Our results also showed a higher percentage of  (23). A similar effect of LPA on mouse and human follicular development has been shown in studies by others (31,32). Their analyses showed that the Bax/Bcl2 ratio was lower in ovaries that were treated with LPA compared to the non-treated groups. They revealed that LPA, as a survival factor, enhanced follicular development in mouse and human ovaries by balancing between the antiand pro-apoptotic genes (31,32). Similarly, it was shown that LPA enhanced BCL2 mRNA levels and decreased the BAX/BCL2 ratio in blastocysts (24,38).
Also, in confirmation of our observation, the molecular analysis showed that the developmental genes GDF9 and BMP15 were not changed in the vitrified group. In contrast to our previous studies, we demonstrated the adverse effects of the vitrification procedure on the follicular cell survival and development after one wk of in vitro culture of mouse whole ovaries (4,29,30,39). Moreover, some alterations in the LIF receptor genes and apoptosis-related genes (except p53) were observed in the vitrified group that was treated with LPA. In agreement with these data, the flow cytometry analysis demonstrated an increase in necrotic cells in the vitrified groups. It seems that these alterations at the molecular levels were improved during culture and there were no adverse effects on the final development to antral stages or subsequent embryonic development.
These controversial results may have been related to ignoring the damaged follicles after mechanical isolation therefore, at the final stage of experiment the intact and normal follicles were selected and studied, so this group of follicles was not shown the negative effect of vitrification.


Virtue of Coenzyme Q10 as an Antioxidant Drug in Periodontal Disease : A Review

Periodontal disease is a disease of inflammatory origin. In the oxidative phosphorylation process for ATP generation, coenzyme Q10 (CoQ10) plays a vital role. Coenzyme Q10 is a vital antioxidant. In cellular bioenergetics, the coenzyme Q10 plays a major role. Because of its large molecular weight and its medicinal properties, the absorption of dietary coenzyme Q10 has numerous therapeutic application in human diseases. Evidence of beneficial effect in cardiovascular and neuro degenerative disease were noted and it was found that Q10 has superior safety record. This article gives the relation of CoQ10 to the periodontal disease.

INTRODUCTION
Periodontal disease is an inflammatory origin arising from the presence of dental plaque and thereby leading to the loss of tooth supporting structures [1]. Periodontal pathogens play a very important role in the etiology of periodontal disease. The destruction of the periodontal tissues is especially mediated by the host derived inflammatory mediators [2]. Arrays of molecules are considered to mediate the inflammatory response at one time or another,

Review Article
among which the free radicals and reactive oxygen species (ROS) plays a prominent role [3]. Periodontal pathogens can induce ROS overproduction, thereby causing collagen and periodontal cell breakdown. When antioxidants scavenges ROS, there is a decreased collagen degradation. Periodontal therapy aims at not only to limit the destruction of periodontal tissues, but also to rebuild the lost periodontal tissues [4]. The limitation of mechanical debridement introduced the administration of systemic and local administration of drugs to treat periodontal disease [5]. Currently interest has been shifted towards treating the host components that leads to the periodontal disease [6]. Ubiquinol which is a reduced form of coenzyme Q 10 acts as an endogenous antioxidant by increasing the concentration of CoQ10 in the diseased gingiva thereby playing a role in effectively suppressing the advanced form of periodontal diseases [7].

STRUCTURE OF COENZYME Q10
Two groups were isolated and characterised in coenzyme Q10, of which one group uses the term "COENZYME Q10" and the other group uses the term "UBIQUINONE" also known as "ubiquitous quinone" [8]. Coenzyme Q10 has a "head and tail" structure and the long isoprenoid side chain keeps it in the mitochondrial or cytoplasmic membrane.
Chemical synthesis, semi-chemical synthesis, and microbial conversion are the methods for producing coenzyme Q10 [9]. Coenzyme Q is found in all cell membrane. The enzymes involved in coenzyme Q oxidation reduction have well-defined protein binding sites in mitochondria. A decrease in coenzyme Q in serum or tissue can be caused by genetic mutation, ageing, cancer, or statin type drugs [10]. Coenzyme Q is synthesised in all cells and under normal conditions; local tissue production is adequate to meet cellular demands. A decrease in the amount of coQ impairs respiratory function and the functioning of antioxidants [11].

DEFICIENCY OF COENZYMEQ10
Because coQ10 is synthesised from scratch in all tissues, it is assumed that they are not dependent on an exogenous supply of coQ10. Despite the fact coQ10 can be synthesised "in vivo" there may be time when the bodies synthetic capacity becomes insufficient to meet the requirements coQ10. In metabolically active cells (such as those in the heart, immune system, gingiva and gastric mucosa) there is a greater susceptibility to coQ10 deficiency and this becomes greater as the cells require increased amount of coQ10 [12]. Deficiency may result from inadequate synthesis as a result of nutritional deficiency, synthesis flaw, either genetic or acquired, increased tissue requirement as a result of illness, COQ10 level decrease due to age increases.

PHARMACOKINETICS
CoQ10 is now recommended as a supplement to traditional cardiovascular disease treatment [13]. Despite being a lipophilic compound, coQ10 solubility is extremely limited, and preparation exhibits a low bioavailability [14]. Physiochemical properties has influenced its absorption, so preparation of coenzymeQ10 has varying bioavailability in powder form, in suspension, oil form, or solubilized from.
Solubilized coenzyme Q10 is clearly preferred due to its better absorption, higher plasma concentration being 2-2.5 times higher during long-term oral therapy with solubilized forms [15] and bioavailability being 3-6 times higher in comparison to powder [16]. The pharmacokinetic advantages of the solubilized form account for its high efficiency as a cardio-protector. Long term management increased (2.5 times) the levels of coenzymeQ10 in plasma and increased concentration in rat myocardium, due to which the survival of cardiomyocyte under ischemia increased and there is a gradual decrease in the size of the necrotic zone of postinfarction [17].

ROLE OF COENZYME Q10 IN PERIODONTAL HEALTH
Chronic periodontics is caused by subgingival plaque accumulation. The microflora of this plaque is extremely complex, making it difficult to determine which organisms are responsible for the disease tissue destruction. Despite these issues, researchers agree on one point that is the subgingival flora [18]. Inflammation is the reaction of organisms to a noxious stimulus, whether mechanical, chemical or infectious. It is a localised protective response elicited by tissue injury or destruction that serves to destroy, dilute or wall off the injurious agent as well as the injured tissue.
Inflammation whether acute or chronic, is dependent on regulated humoral and cellular responses. Coenzyme Q10 deficiency at its enzyme sites in gingival tissue may exist independently of and as a result of periodontal disease. Nutritional deficiencies and not periodontal disease, could exacerbate the deficiency of gingival coenzyme Q10.
Oral dental treatment and oral hygiene could correct the plaque and calculus, but not the part of the coQ10 deficiency caused by a systemic cause. CoQ10 therapy can be combined with oral hygiene for an improved treatment of this type of periodontal disease. The specific activity of succinic dehydrogenase-coenzyme Q10 reductase in gingival tissues from patient with periodontal disease was compared to normal periodontal tissues using biopsies which revealed that in patients with periodontal disease there is a decreased level CoQ10.
In a deficient patient, exogenous coQ10 administration increased the specific activity of this mitochondrial enzyme [19]. The periodontal score was also reduced implying that coQ10 should be considered as an adjunct for periodontal treatment for current dental practice [20]. The coenzymeQ10 may reduce gingival inflammation without affecting Gingival Crevicular Fluid) GCF antioxidant levels [21]. Another study found a significant reduction in Thio Barbituric Acid Reactive Substance (TBRAS) in GCF of patients who received scaling and root planning with CoQ10 [22]. The effect of coQ10 application to the periodontal pocket with or without subgingival mechanical debridement have been studied and it was found that, during the first three weeks at the experimental sites there was a reduction in gingival crevicular fluid flow, probing depth, and attachment loss significantly, with a significant improvement in modified gingival index, bleeding on probing, and peptidase activity derived from periodontopathic bacteria were observed [23]. It suggested that the research literature on coenzymeQ10 periodontal effect does not extend to international English language dental literature.
Coenzyme Q10 deficiency has been discovered in the gingiva of the patient with periodontal disease [24]. In contrast to patient with normal periodontal tissues gingival biopsies from patient with inflamed periodontal tissues revealed a deficiency of coQ10.

CONCLUSION
Various studies have reported the beneficial effects of Coenzyme Q10 in relation to periodontal disease. Although coenzyme Q10 was once thought to be an alternative medication, it is now used routinely by many dentists and periodontists, both topically and systemically. However there is a scarcity of new data on the use of coenzyme Q10 in the treatment of periodontal diseases. Therefore further studies are needed to evaluate the efficacy of the local administration of coenzymeQ10 in patients with periodontal disease.

CONSENT
It is not applicable.

ETHICAL APPROVAL
It is not applicable.